47 citations · 143 across the 13 of their papers we have counts for
13 papers
Selective Pre-training for Private Fine-tuning
Da Yu, Sivakanth Gopi, Janardhan Kulkarni +5
Text prediction models, when used in applications like email clients or word processors, must protect user data privacy and adhere to model size constraints. These constraints are…
Individual Privacy Accounting for Differentially Private Stochastic Gradient Descent
Da Yu, Gautam Kamath, Janardhan Kulkarni +3
Differentially private stochastic gradient descent (DP-SGD) is the workhorse algorithm for recent advances in private deep learning. It provides a single privacy guarantee to all d…
Anomaly Detection by Leveraging Incomplete Anomalous Knowledge with Anomaly-Aware Bidirectional GANs
Bowen Tian, Qinliang Su, Jian Yin
The goal of anomaly detection is to identify anomalous samples from normal ones. In this paper, a small number of anomalies are assumed to be available at the training stage, but t…
SHGNN: Structure-Aware Heterogeneous Graph Neural Network
Wentao Xu, Yingce Xia, Weiqing Liu +3
Many real-world graphs (networks) are heterogeneous with different types of nodes and edges. Heterogeneous graph embedding, aiming at learning the low-dimensional node representati…
KGE-CL: Contrastive Learning of Tensor Decomposition Based Knowledge Graph Embeddings
Zhiping Luo, Wentao Xu, Weiqing Liu +3
Learning the embeddings of knowledge graphs (KG) is vital in artificial intelligence, and can benefit various downstream applications, such as recommendation and question answering…
Availability Attacks Create Shortcuts
Da Yu, Huishuai Zhang, Wei Chen +2
Availability attacks, which poison the training data with imperceptible perturbations, can make the data \emph{not exploitable} by machine learning algorithms so as to prevent unau…
Improved Drug-target Interaction Prediction with Intermolecular Graph Transformer
Siyuan Liu, Yusong Wang, Tong Wang +6
The identification of active binding drugs for target proteins (termed as drug-target interaction prediction) is the key challenge in virtual screening, which plays an essential ro…
HIST: A Graph-based Framework for Stock Trend Forecasting via Mining Concept-Oriented Shared Information
Wentao Xu, Weiqing Liu, Lewen Wang +4
Stock trend forecasting, which forecasts stock prices' future trends, plays an essential role in investment. The stocks in a market can share information so that their stock prices…
Instance-wise Graph-based Framework for Multivariate Time Series Forecasting
Wentao Xu, Weiqing Liu, Jiang Bian +2
The multivariate time series forecasting has attracted more and more attention because of its vital role in different fields in the real world, such as finance, traffic, and weathe…
Large Scale Private Learning via Low-rank Reparametrization
Da Yu, Huishuai Zhang, Wei Chen +2
We propose a reparametrization scheme to address the challenges of applying differentially private SGD on large neural networks, which are 1) the huge memory cost of storing indivi…
REST: Relational Event-driven Stock Trend Forecasting
Wentao Xu, Weiqing Liu, Chang Xu +3
Stock trend forecasting, aiming at predicting the stock future trends, is crucial for investors to seek maximized profits from the stock market. Many event-driven methods utilized…
How Does Data Augmentation Affect Privacy in Machine Learning?
Da Yu, Huishuai Zhang, Wei Chen +2
It is observed in the literature that data augmentation can significantly mitigate membership inference (MI) attack. However, in this work, we challenge this observation by proposi…
SEEK: Segmented Embedding of Knowledge Graphs
Wentao Xu, Shun Zheng, Liang He +3
In recent years, knowledge graph embedding becomes a pretty hot research topic of artificial intelligence and plays increasingly vital roles in various downstream applications, suc…